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Critical Evaluation of Different Biomarkers and Machine-Learning-Based Approaches to Identify Dementia Disease in Early Stages

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Fourth International Congress on Information and Communication Technology

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1041))

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Abstract

Dementia is the loss of cognitive functioning and behavioural abilities to some extent. This is a major neurocognitive disorder which is a group of symptoms caused by other different conditions. Alzheimer’s disease has considered as the most common type of dementia. Apart from that, vascular dementia (VD), Lewy body dementia (DLB), frontotemporal dementia (FTD), Parkinson’s disease dementia, normal pressure hydrocephalus (NPH), Creutzfeldt–Jakob disease and syphilis are under Dementia. The cure for this disease is yet to be found, hence recognizing the disease in early stages and delaying the progress is a highly important fact. So, the investigation of this disease will remain as an open challenge. The aim of this paper is to review biomarkers and selected machine-learning techniques that can be segregated into early detection of dementia. Various machine-learning techniques such as artificial neural networks, decision trees and support vector machine will be discussed in this paper to find a better approach to identify Dementia in early stages. Especially this paper is consisting of following sections: (i) A brief description of Dementia and each type and the global statistics; (ii) A review of various type of medical techniques to identify dementia (MRI, CT, SPECT, fMRI, PET, EEG and CSF); (iii) Pre-processing signals; (iv) A review of machine-learning techniques.

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Acknowledgements

I would like to express my sincere gratitude to my supervisor Mr. Achala Chathuranga Aponso for providing me continuous support and guidance towards this research. And also, special gratitude for Ahila Arumugam as her research paper on dementia helped me to continue my research work. Special thanks to Informatics Institute of Technology and University of Westminster.

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Authors and Affiliations

  1. Informatics Institute of Technology, University of Westminster, Colombo, Sri Lanka

    Gayakshika Gimhani, Achala Chathuranga Aponso & Naomi Krishnarajah

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  1. Gayakshika Gimhani
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  2. Achala Chathuranga Aponso
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  3. Naomi Krishnarajah
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Correspondence to Gayakshika Gimhani .

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Editors and Affiliations

  1. School of Science and Technology, Middlesex University, London, UK

    Xin-She Yang

  2. University of Reading, Reading, UK

    Simon Sherratt

  3. Department of Information Technology, Techno India College of Technology, Kolkata, West Bengal, India

    Nilanjan Dey

  4. Global Knowledge Research Foundation, Ahmedabad, Gujarat, India

    Amit Joshi

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Gimhani, G., Aponso, A.C., Krishnarajah, N. (2020). Critical Evaluation of Different Biomarkers and Machine-Learning-Based Approaches to Identify Dementia Disease in Early Stages. In: Yang, XS., Sherratt, S., Dey, N., Joshi, A. (eds) Fourth International Congress on Information and Communication Technology. Advances in Intelligent Systems and Computing, vol 1041. Springer, Singapore. https://doi.org/10.1007/978-981-15-0637-6_30

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  • DOI: https://doi.org/10.1007/978-981-15-0637-6_30

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